Update app.py

app.py

@@ -160,7 +160,7 @@ def save_results_to_pdf(test_results, filename):
         return f"Error saving PDF: {str(e)}", None
 
 # Build health card layout
-def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id="", pdf_filepath=""):
+def build_health_card(profile_image, test_results, summary, patient_name="", patient_age="", patient_gender="", patient_id=""):
     from datetime import datetime
     current_date = datetime.now().strftime("%B %d, %Y")
     
@@ -203,11 +203,12 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
         </div>
 
         <div style="display: flex; gap: 15px; justify-content: center; flex-wrap: wrap;">
-            <a href="{pdf_filepath}" download="Health_Report.pdf">
-                <button style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;">
-                    📥 Download Report
-                </button>
-            </a>
+            <button onclick="window.print()" style="padding: 12px 24px; background: linear-gradient(135deg, #4caf50, #45a049); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(76, 175, 80, 0.3); transition: all 0.3s;">
+                📥 Download Report
+            </button>
+            <button style="padding: 12px 24px; background: linear-gradient(135deg, #2196f3, #1976d2); color: white; border: none; border-radius: 8px; cursor: pointer; font-weight: 600; font-size: 14px; box-shadow: 0 4px 12px rgba(33, 150, 243, 0.3);">
+                📞 Find Labs Near Me
+            </button>
         </div>
     </div>
     """
@@ -217,27 +218,28 @@ def build_health_card(profile_image, test_results, summary, patient_name="", pat
 current_patient_details = {'name': '', 'age': '', 'gender': '', 'id': ''}
 
 # Modified analyze_face function
-def analyze_face(inputs):
-    image = inputs[0]
-    patient_name = inputs[1]
-    patient_age = inputs[2]
-    patient_gender = inputs[3]
-    patient_id = inputs[4]
-    mode = inputs[5]
-    
-    if mode == "Image" and image is None:
-        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+def analyze_face(input_data):
+    if isinstance(input_data, str):  # Video input (file path in Replit)
+        cap = cv2.VideoCapture(input_data)
+        if not cap.isOpened():
+            return "<div style='color:red;'>⚠️ Error: Could not open video.</div>", None
+        ret, frame = cap.read()
+        cap.release()
+        if not ret:
+            return "<div style='color:red;'>⚠️ Error: Could not read video frame.</div>", None
+    else:  # Image input
+        frame = input_data
+        if frame is None:
+            return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
 
     # Resize image to reduce processing time
-    frame = cv2.resize(image, (640, 480))  # Adjust resolution for Replit
+    frame = cv2.resize(frame, (640, 480))  # Adjust resolution for Replit
     frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
     result = face_mesh.process(frame_rgb)
-    
     if not result.multi_face_landmarks:
         return "<div style='color:red;'>⚠️ Error: Face not detected.</div>", None
     landmarks = result.multi_face_landmarks[0].landmark  # Fixed: Use integer index
     features = extract_features(frame_rgb, landmarks)
-    
     test_values = {}
     r2_scores = {}
 
@@ -251,6 +253,21 @@ def analyze_face(inputs):
             test_values[label] = value
             r2_scores[label] = 0.0
 
+    gray = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2GRAY)
+    green_std = np.std(frame_rgb[:, :, 1]) / 255
+    brightness_std = np.std(gray) / 255
+    tone_index = np.mean(frame_rgb[100:150, 100:150]) / 255 if frame_rgb[
+        100:150, 100:150].size else 0.5
+    hr_features = [brightness_std, green_std, tone_index]
+    heart_rate = float(np.clip(hr_model.predict([hr_features])[0], 60, 100))
+    skin_patch = frame_rgb[100:150, 100:150]
+    skin_tone_index = np.mean(skin_patch) / 255 if skin_patch.size else 0.5
+    brightness_variation = np.std(cv2.cvtColor(frame_rgb,
+                                               cv2.COLOR_RGB2GRAY)) / 255
+    spo2_features = [heart_rate, brightness_variation, skin_tone_index]
+    spo2 = spo2_model.predict([spo2_features])[0]
+    rr = int(12 + abs(heart_rate % 5 - 2))
+
     test_results = {
         "Hematology":
         build_table("🩸 Hematology",
@@ -274,9 +291,12 @@ def analyze_face(inputs):
                      ("Potassium", test_values["Potassium"], (3.5, 5.1))]),
         "Vitals":
         build_table("❤️ Vitals",
-                    [("SpO2", 98, (95, 100)),
-                     ("Heart Rate", 72, (60, 100)),
-                     ("Temperature", 98.6, (97, 99))])
+                    [("SpO2", spo2, (95, 100)),
+                     ("Heart Rate", heart_rate, (60, 100)),
+                     ("Respiratory Rate", rr, (12, 20)),
+                     ("Temperature", test_values["Temperature"], (97, 99)),
+                     ("BP Systolic", test_values["BP Systolic"], (90, 120)),
+                     ("BP Diastolic", test_values["BP Diastolic"], (60, 80))])
     }
 
     summary = "<ul><li>Your hemoglobin is a bit low — this could mean mild anemia.</li><li>Low iron storage detected — consider an iron profile test.</li><li>Elevated bilirubin — possible jaundice. Recommend LFT.</li><li>High HbA1c — prediabetes indication. Recommend glucose check.</li><li>Low SpO₂ — suggest retesting with a pulse oximeter.</li></ul>"
@@ -284,32 +304,53 @@ def analyze_face(inputs):
     _, buffer = cv2.imencode('.png', frame_rgb)
     profile_image_base64 = base64.b64encode(buffer).decode('utf-8')
 
-    # Create a temporary file path for the PDF
-    pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
-    pdf_filepath = f"/tmp/{pdf_filename}"
-
-    # Save the PDF
-    pdf_result, _ = save_results_to_pdf(test_results, pdf_filepath)
-
     # Use global patient details
+    global current_patient_details
     health_card_html = build_health_card(
         profile_image_base64, 
         test_results,
         summary, 
-        patient_name,
-        patient_age,
-        patient_gender,
-        patient_id,
-        pdf_filepath=pdf_filepath
+        current_patient_details['name'],
+        current_patient_details['age'],
+        current_patient_details['gender'],
+        current_patient_details['id']
     )
     
-    return health_card_html, pdf_filepath
+    # Generate PDF and return for download
+    pdf_filename = f"Health_Report_{datetime.now().strftime('%Y-%m-%d_%H-%M-%S')}.pdf"
+    pdf_result, pdf_filepath = save_results_to_pdf(test_results, pdf_filename)
+    
+    if pdf_filepath:
+        # Copy the PDF to a temporary directory for Gradio to serve it
+        temp_pdf_path = "/tmp/" + os.path.basename(pdf_filepath)
+        shutil.copy(pdf_filepath, temp_pdf_path)
+        
+        return health_card_html, temp_pdf_path
 
 
-# Gradio interface setup
-with gr.Blocks() as demo:
-    gr.Markdown("""# 🧠 Face-Based Lab Test AI Report""")
+# Modified route_inputs function
+def route_inputs(mode, image, video, patient_name, patient_age, patient_gender, patient_id):
+    if mode == "Image" and image is None:
+        return "<div style='color:red;'>⚠️ Error: No image provided.</div>", None
+    if mode == "Video" and video is None:
+        return "<div style='color:red;'>⚠️ Error: No video provided.</div>", None
     
+    # Store patient details globally for use in analyze_face
+    global current_patient_details
+    current_patient_details = {
+        'name': patient_name,
+        'age': patient_age, 
+        'gender': patient_gender,
+        'id': patient_id
+    }
+    
+    health_card_html, pdf_file_path = analyze_face(image if mode == "Image" else video)
+    return health_card_html, pdf_file_path
+
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("""# 🧠 Face-Based Lab Test AI Report (Video Mode)""")
     with gr.Row():
         with gr.Column():
             gr.Markdown("### Patient Information")
@@ -330,8 +371,8 @@ with gr.Blocks() as demo:
             result_html = gr.HTML(label="🧪 Health Report Table")
             result_pdf = gr.File(label="Download Health Report PDF", interactive=False)
 
-            submit_btn.click(fn=analyze_face,
-                             inputs=[image_input, patient_name, patient_age, patient_gender, patient_id, mode_selector],
+            submit_btn.click(fn=route_inputs,
+                             inputs=[mode_selector, image_input, video_input, patient_name, patient_age, patient_gender, patient_id],
                              outputs=[result_html, result_pdf])
 
 # Launch Gradio for Replit